Public and private sector users and organizations generate large amount of information data items and objects that are amenable to Extract, Transform and Load (ETL) transactions to process, understand and otherwise utilize the underlying information. ETL refers to a process in database usage and especially in data warehousing that “extracts” data from homogeneous or heterogeneous data sources, “transforms” the data for storing it in a specified or desired format or structure for querying and analysis purposes, and “loads” the transformed data onto a designated target database destination, such as an operational data storage device (a “store”), a data warehouse, a data mart, etc. Some implementations perform (execute) all three ETL phases in parallel with respect to different data items, enabling resource and time efficiencies.
In development and testing of relational database modifications, batch command files are assembled and are tested in an iterative process until all errors are identified and corrected. Some of these commands, such as a table creation, when previously run successfully, will cause an error when an attempt is made to run the same command a subsequent time. These commands may not need to be repeated, and therefore should not be, in order to avoids such errors.
Prior art methods accommodate potential problems with recreating an already-existing table, such as the Structured Query Language (“SQL”) command to “create if not exists” a table in some database management systems, such as MySQL™. (MYSQL is a trademark or registered trademark of Oracle and/or its affiliates in the United States or other countries.) Other prior art methods indicate that a subset of the commands should not be run, for example, by commenting out the commands to be bypassed.